LAUSR

Several players are enabled in smart grids and involved in decision-making process. These players are fully interconnected in the transactive energy (TE) framework to operate the system optimally. By implementing the TE, the final decision are adopted after various consensus between participants. Hence, huge number of data and information are communicated in this framework which leads to complex communication algorithms and wide-bandwidth channels. Besides, the prosumers dataset in the TE framework are with low level of sparsity which is due to their independent interactions with peers and the grid. This article proves the low sparsity in the prosumers dataset and shows the shortcomings of the existing methods. A dynamic intelligent algorithm is proposed in this article to characterize the prosumers data based on the mutual information (MI) theorem. In addition, two data compression algorithms have been proposed in this article to reduce the bandwidth and space for communicating and storing purposes, respectively. The proposed algorithms for data modeling and compression provide compatible superlative performance with minimum information loss. This article saves more communication channel bandwidth by comparing the conventional methods. Fast and robust adaptation of the proposed algorithm facilitates the practical implementation of energy management in the TE framework when wide data transmission is needed. The performance of the proposed algorithms has been evaluated using simulated prosumers dataset and real-world residential and industrial consumer data.